Meiling Zhu

Yinfeng Yu, Hailong Zhang, Meiling Zhu

Audiovisual embodied navigation enables robots to locate audio sources by dynamically integrating visual observations from onboard sensors with the auditory signals emitted by the target. The core challenge lies in effectively leveraging multimodal cues to guide navigation. While prior works have explored basic fusion of visual and audio data, they often overlook deeper perceptual context. To address this, we propose the Dynamic Multi-Target Fusion for Efficient Audio-Visual Navigation (DMTF-AVN). Our approach uses a multi-target architecture coupled with a refined Transformer mechanism to filter and selectively fuse cross-modal information. Extensive experiments on the Replica and Matterport3D datasets demonstrate that DMTF-AVN achieves state-of-the-art performance, outperforming existing methods in success rate (SR), path efficiency (SPL), and scene adaptation (SNA). Furthermore, the model exhibits strong scalability and generalizability, paving the way for advanced multimodal fusion strategies in robotic navigation. The code and videos are available at https://github.com/zzzmmm-svg/DMTF.

Dongsheng Yang, Meiling Zhu, Yinfeng Yu

Vision-and-language navigation is one of the core tasks in embodied intelligence, requiring an agent to autonomously navigate in an unfamiliar environment based on natural language instructions. However, existing methods often fail to match instructions with environmental information in complex scenarios, one reason being the lack of common-sense reasoning ability. This paper proposes a vision-and-language navigation method called Landmark-Guided Knowledge (LGK), which introduces an external knowledge base to assist navigation, addressing the misjudgment issues caused by insufficient common sense in traditional methods. Specifically, we first construct a knowledge base containing 630,000 language descriptions and use knowledge Matching to align environmental subviews with the knowledge base, extracting relevant descriptive knowledge. Next, we design a Knowledge-Guided by Landmark (KGL) mechanism, which guides the agent to focus on the most relevant parts of the knowledge by leveraging landmark information in the instructions, thereby reducing the data bias that may arise from incorporating external knowledge. Finally, we propose Knowledge-Guided Dynamic Augmentation (KGDA), which effectively integrates language, knowledge, vision, and historical information. Experimental results demonstrate that the LGK method outperforms existing state-of-the-art methods on the R2R and REVERIE vision-and-language navigation datasets, particularly in terms of navigation error, success rate, and path efficiency.